Applying Machine Learning and Neural Networks to Approximate Libraries of Constants in a Few-Group Library of Neutronic Constants to Simulate RBMK-1000

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Applying Machine Learning and Neural Networks to Approximate Libraries of Constants in a Few-Group Library of Neutronic Constants to Simulate RBMK-1000

Ivanov, I. E.; Bychkov, S. A.; Grushin, N. A.; Druzhinin, V. E.; Lysov, D. A.; Plekhanov, R. V.; Shchukin, N. V.

Physics of atomic nuclei, 12/2022, Volume: 85, Issue: 8

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The paper describes a qualitatively new CNET library of few-group neutron cross sections designed to simulate neutronic parameters in RBMK-1000 reactors. The CNET library makes use of a neural network to approximate cell (node) constants registered with a large group of full-scale calculations of various reactor states. We explore problems faced when approximating neutron cross sections using machine learning techniques and, specifically, neural networks. An approach is described to solve these problems following JSC VNIIAES guidelines to develop a new CNET library. The results of validating the MNT-CUDA high-accuracy program featuring the CNET library are presented.

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